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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Alan Saul 2015-04-28 10:51:23 +01:00
commit 5dd75d66ae
6 changed files with 71 additions and 14 deletions
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4
GPy/core/gp.py
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@ -395,7 +395,7 @@ class GP(Model):
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx'):
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx', predict_kw=None):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -444,7 +444,7 @@ class GP(Model):
which_data_ycols, fixed_inputs,
levels, samples, fignum, ax, resolution,
plot_raw=plot_raw, Y_metadata=Y_metadata,
data_symbol=data_symbol, **kw)
data_symbol=data_symbol, predict_kw=predict_kw, **kw)
def input_sensitivity(self, summarize=True):
"""

14
GPy/core/sparse_gp.py
View file

@ -132,12 +132,14 @@ class SparseGP(GP):
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
var = var
else:
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0)
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
@ -147,9 +149,9 @@ class SparseGP(GP):
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
else:
psi0_star = self.kern.psi0(self.Z, Xnew)
psi1_star = self.kern.psi1(self.Z, Xnew)
#psi2_star = self.kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
psi0_star = kern.psi0(self.Z, Xnew)
psi1_star = kern.psi1(self.Z, Xnew)
#psi2_star = kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
la = self.posterior.woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions?
@ -161,7 +163,7 @@ class SparseGP(GP):
for i in range(Xnew.shape[0]):
_mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]]
psi2_star = self.kern.psi2(self.Z, NormalPosterior(_mu, _var))
psi2_star = kern.psi2(self.Z, NormalPosterior(_mu, _var))
tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
var_ = mdot(la.T, tmp, la)

7
GPy/core/verbose_optimization.py
View file

@ -141,6 +141,13 @@ class VerboseOptimization(object):
def finish(self, opt):
self.status = opt.status
if self.verbose and self.ipython_notebook:
if 'conv' in self.status.lower():
self.progress.bar_style = 'success'
elif self.iteration >= self.maxiters:
self.progress.bar_style = 'warning'
else:
self.progress.bar_style = 'danger'
def __exit__(self, type, value, traceback):
if self.verbose: